EventPointMesh: Human Mesh Recovery Solely From Event Point Clouds.

How much can we infer about human shape using an event camera that only detects the pixel position where the luminance changed and its timestamp? This neuromorphic vision technology captures changes in pixel values at ultra-high speeds, regardless of the variations in environmental lighting brightness. Existing methods for human mesh recovery (HMR) from event data need to utilize intensity images captured with a generic frame-based camera, rendering them vulnerable to low-light conditions, energy/memory constraints, and privacy issues. In contrast, we explore the potential of solely utilizing event data to alleviate these issues and ascertain whether it offers adequate cues for HMR, as illustrated in Fig.

ProDebNet: projector deblurring using a convolutional neural network.

Projection blur can occur in practical use cases that have non-planar and/or multi-projection display surfaces with various scattering characteristics because the surface often causes defocus and subsurface scattering. To address this issue, we propose ProDebNet, an end-to-end real-time projection deblurring network that synthesizes a projection image to minimize projection blur. The proposed method generates a projection image without explicitly estimating any geometry or scattering characteristics of the projection screen, which makes real-time processing possible.

Making Graphical Information Visible in Real Shadows on Interactive Tabletops.

We introduce a shadow-based interface for interactive tabletops. The proposed interface allows a user to browse graphical information by casting the shadow of his/her body, such as a hand, on a tabletop surface. Central to our technique is a new optical design that utilizes polarization in addition to the additive nature of light so that the desired graphical information is displayed only in a shadow area on a tabletop surface.